In order to mitigate moving-object interference and improve robustness and localization accuracy of visual SLAM (simultaneous localization and mapping) systems in dynamic environments, a visual SLAM method integrating object detection and geometric constraints, termed YOLO-GK-SLAM, was used to investigate accurate localization and reliable mapping in dynamic scenes. First, based on the ORB-SLAM3 framework, a dynamic feature point removal mechanism integrating semantic object detection with multi-view geometric constraints was designed to achieve accurate elimination of dynamic feature points and re-identification of static information within dynamic regions. Second, a refined keyframe selection strategy was proposed to reduce candidate keyframes while preserving salient static landmarks, thereby improving the efficiency of loop closure detection and pose optimization. The results show that, on the TUM dynamic dataset, the proposed YOLO-GK-SLAM method reduces ATE (absolute trajectory error) and RPE (relative pose error) by at least 14.34% and 17.48%, respectively, compared with classical visual SLAM methods, while significantly improving map completeness and boundary clarity. It is concluded that the proposed method maintains high localization accuracy and stability in real-world dynamic environments, effectively alleviates the influence of moving objects, and significantly enhances the robustness and practical applicability of visual SLAM systems.